Enoka Roger M
Department of Integrative Physiology, University of Colorado, Boulder, CO, USA.
J Electromyogr Kinesiol. 2019 Jun;46:70-83. doi: 10.1016/j.jelekin.2019.03.010. Epub 2019 Mar 21.
Advances in technology have ushered in a new era in the measurement and interpretation of surface-recorded electromyographic (EMG) signals. These developments have included improvements in detection systems, the algorithms used to decompose the interference signals, and the strategies used to edit the identified waveforms. To evaluate the validity of the results obtained with this new technology, the purpose of this review was to compare the results achieved by decomposing surface-recorded EMG signals into the discharge times of single motor units with what is known about the rate coding characteristics of single motor units based on recordings obtained with intramuscular electrodes. The characteristics compared were peak discharge rate, saturation of discharge rate during submaximal contractions, rate coding during fast contractions, the association between oscillations in force and discharge rate, and adjustments during fatiguing contractions. The comparison indicates that some decomposition methods are able to replicate many of the findings derived from intramuscular recordings, but additional improvements in the methods are required. Critically, more effort needs to be focused on editing the waveforms identified by the decomposition algorithms. With adequate attention to detail, this technology has the potential to augment our knowledge on motor unit physiology and to provide useful approaches that are being translated into clinical practice.
技术进步开启了表面记录肌电图(EMG)信号测量与解读的新时代。这些进展包括检测系统的改进、用于分解干扰信号的算法以及用于编辑识别波形的策略。为评估这项新技术所获结果的有效性,本综述旨在比较将表面记录的EMG信号分解为单个运动单位放电时间所得到的结果,与基于肌内电极记录所了解的单个运动单位速率编码特征。所比较的特征包括峰值放电率、次最大收缩期间放电率的饱和度、快速收缩期间的速率编码、力量振荡与放电率之间的关联以及疲劳收缩期间的调整。比较表明,一些分解方法能够复制许多从肌内记录得出的结果,但方法仍需进一步改进。至关重要的是,需要更多精力集中在编辑由分解算法识别出的波形上。若充分关注细节,这项技术有潜力扩充我们对运动单位生理学的认识,并提供正被转化为临床实践的有用方法。